A 6-point scorecard for AI-ready product pages

AI search engines like ChatGPT, Google AI Mode, and Perplexity are changing how consumers discover and purchase products online. If your product pages aren’t optimized for these AI assistants, you could be missing out on a growing source of traffic and revenue.

The challenge? AI assistants don’t evaluate product pages in the same way traditional search engines do. They need to fully understand your products so they can confidently recommend them to different users with different needs.

To help you assess how well your product pages are optimized for AI search, here’s a simple scorecard covering the six most important factors. 

1. Product specifications

Does the product page clearly display the product’s attributes and specifications?

AI assistants need clearly stated specifications to better understand your products and match them to customer needs. If a shopper asks an AI assistant for “an airline-friendly crate for a 115-pound dog,” the AI must be able to see the maximum weight limit of a product before it will recommend it. Without clear specifications, some products won’t get recommended, even if they’re actually a perfect match.

Amazon does this really well, and it’s likely one of the many keys to their strong performance in AI search. Just look at all the helpful specifications they clearly lay out on their product pages.

Action item: Go through your product pages and make certain all applicable specifications are clearly displayed. Don’t bury them in the main product description or other marketing copy. Clearly lay them out in a structured table or bulleted list.

2. Unique selling points

Are the product’s unique benefits clearly described?

AI needs to understand both what makes your product stand out and why your products should be recommended over the competition. If a product page reads like every other industry website, AI assistants have no compelling reason to recommend the listed products.

Think about it from the AI’s perspective: If a user asks “what’s the best L-shaped sofa,” the AI will look for products with clear differentiators (hidden storage, machine-washable, modular parts, durability, etc.). The characteristics that make your product stand out should be explicitly stated on the page.

Here’s a great example from Home Reserve. Their product pages have a section called “Key Features” that lists the unique selling points that separate them from the competition.

Action item: Make sure your product pages clearly state what makes them better and why it matters to the customer. Keep your key features specific. Generic selling points like “high-quality craftsmanship” or “premium materials” are too vague and don’t give AI assistants enough information to establish a clear differentiation.

Dig deeper: How AI-driven shopping discovery changes product page optimization

3. Use cases and target audience

Are the product’s intended use cases and audience clear?

AI assistants don’t match products to keywords — they match products to people and their unique needs. When a user asks ChatGPT, “what’s the best desk for a small apartment,” the AI looks for products intended for compact spaces, small rooms, or apartment living.

If a product page only describes the desk’s dimensions without connecting them to a particular use case, AI assistants may not recommend the product when users ask about those scenarios.

Any given product could have a multitude of use cases and audiences. A standing desk could be ideal for remote workers, people with back pain, gamers, or small business owners outfitting a home office. If a product page only speaks to one of these audiences, it might not get recommended to the others in AI search.

Action item: For each product, include the top three to five specific use cases or audience segments on the page. Go beyond demographics and think about situations, pain points, and goals. 

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4. FAQ section

Does the product page include an FAQ section answering common questions about the product?

AI assistants always try to connect products with the right buyer. When a user asks a question like, “what’s the best waterproof sealant for a flat roof,” the AI looks for information on product pages demonstrating they’re a good fit for the particular use case.

This is what makes FAQ content so valuable. A well-structured FAQ section can give AI assistants additional confidence that the product is a good fit for the user and worthy of a mention. The more specific and detailed your FAQ answers are, the more prompts your product can match within AI search.

For example, Liquid Rubber sells mulch glue and waterproof sealants. They do a great job of providing a clear list of frequently asked questions on their product pages.

This type of FAQ content can help their products get recommended more often when users ask ChatGPT specific questions:

• What’s the best VOC mulch glue?
• Can I get mulch glue that will last up to 12 months?
• Is there a mulch glue that delivers within one week?

Action item: Review your customer support inquiries, product reviews, competitor pages, and relevant Reddit threads to identify the most common customer questions. Then add these questions directly to your product pages with clear and concise answers.

Dig deeper: AI citations favor listicles, articles, product pages: Study

5. Product reviews

Does the product page display customer ratings and review counts?

AI assistants will recommend highly rated products with strong reputations. A product with 500+ reviews and a 4.8-star rating is a much safer recommendation than a product with zero reviews or a low rating.

Just ask ChatGPT for product recommendations, and you’ll see the product ratings front and center. Take, for example, the prompt, “What’s the best medium roast caramel flavored coffee?”

It’s clear that ChatGPT relies heavily on product reviews and only recommends products with a high rating. When you click on any of these products, you’ll see that product ratings and the number of reviews are clearly displayed on the product page.

Note: Your product’s rating in ChatGPT may differ from what’s on your product page. This is because ChatGPT calculates an aggregate rating across multiple merchants (e.g., Walmart, Target, etc.), rather than only pulling from your product page.

But having a strong rating isn’t enough — you need a lot of reviews as well. I recently reviewed 1,000 ecommerce-focused prompts and found that the median number of reviews was 156. So, if you want to increase your chances of getting recommended by ChatGPT (and other AI assistants), aim for at least 150+ product reviews.

Action item: Make sure your product pages clearly display customer ratings, review counts, and (ideally) some actual reviews. Third-party review platforms like Yotpo, Judge.me, and Shopper Approved can solicit product reviews from customers for you.

Dig deeper: How to make ecommerce product pages work in an AI-first world

6. Product structured data

Does the product page include structured data for price, availability, reviews, and other key attributes?

It’s easier for AI search engines to understand information presented in a clear structure (e.g., tables, lists). But there’s nothing more structured than the JSON format for structured data (also known as schema markup).

There’s a common claim in AI SEO that structured data is some kind of magic bullet for AI visibility. The reality is more nuanced.

Structured data experiment

An interesting experiment conducted by SEO consultant Dan Taylor tested the impact of structured data on AI search. He included a physical address for a made-up company in the JSON-LD structured data, but didn’t include it anywhere in the page content itself. Then, when he asked ChatGPT for the address, it still pulled it from the structured data.

This experiment shows that AI assistants are indeed crawling structured data. But they’re not necessarily parsing it the same way a traditional search engine would. Instead, they’re simply treating it as another source of text on the page.

If the content in your schema is relevant to a user’s prompt, AI assistants will pick it up. But it doesn’t matter whether the schema is valid or completely made up.

Where structured data helps most

So, if AI assistants treat structured data like any other text, is it still worth adding it to your product pages? The short answer is “yes.”

Presenting important product information clearly and well formatted can always help AI assistants understand your product pages. But the real advantage is in the product cards found within the AI responses.

Google is using its Knowledge Graph data in their AI systems, and this type of structured data, or schema markup, can feed into it. There are also reports of ChatGPT using Google Shopping data for its product recommendations.

So, the main advantage of structured data is how it plays into Google’s Knowledge Graph of products, which can directly impact product recommendations across Google AI Overviews, AI Mode, and even ChatGPT.

With the rise of agentic commerce, product data will only become more important as AI agents rely on it to compare, evaluate, and even purchase products on behalf of users.

Putting the scorecard to work

Here’s a quick overview you can use to audit your product pages:

Once you’ve scored your highest-priority pages, any gaps become the priority on your AI product optimization roadmap. Tackle the “No” items first, since those represent the biggest missed opportunities, then work on upgrading the “Partial” scores.

This type of product optimization is still a blind spot for many ecommerce brands, which means every factor you improve is a chance to get recommended where they don’t. The sooner you close these gaps, the harder it becomes for competitors to catch up.

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